Let’s take a look at some Python Coding practices that can significantly enhance your Python coding skills.

With Python now being a widely recognized language used in various development domains, it is not surprising that a set of best practices have been established as a foundation for good Python coding.

To make the most of numerous third-party libraries, it is beneficial to consistently apply these industry-recognized Python coding practices.

Maintain Consistent Indentation within a Python File

Python places significant emphasis on indentation, and it is vital not to mix tabs and spaces in the same file, as this can lead to confusion about the level at which a particular line is being considered.

The PEP-8 guide is a great reference to avoid any potential confusion when your code is read or used by others. Additionally, Integrated Development Environments (IDEs) can help format your code and should be utilized to ensure consistency.

Utilize the Python Dictionary

Python’s dictionary data structure offers a simple and effective means of data storage. It is a collection of unordered key-value pairs, with the condition that each key must be unique. Here’s an example:

def count_duplicates(numbers):
    result = {}
    for number in numbers:
        result.setdefault(number, 0)
        result[number] += 1
    return result

In the code above, we use setdefault to ensure that each number has a default count of 0, making the code more concise and clean.

Don’t Skip Inline Comments or Documentation

Failing to comment on complex sections of your code may seem like a time-saver initially, but it can lead to confusion when revisiting the code later. Documenting your code is crucial for understanding its purpose and functionality. In addition to comments, try to self-document your code by giving descriptive names to methods, functions, and variables.

Leverage Python’s Logical Operators

Python’s logical operators can return the actual value of the operation, not just True or False. This feature can make your code more concise and readable. Consider the following example:

def get_obj(): 
    return check_in_cache() or pull_from_db()

This single line of code replaces a four-line code snippet, making it more readable.

Maintain a Well-structured Repository

For any project, starting with a well-structured code repository and version control system is crucial. When working with Python, your repository should include key components such as license, README, module code, setup.py, requirements.txt, documentation, and tests. This structure will help organize your project and streamline the development process.

Use Python Properties Instead of Explicit Getters and Setters

Python properties can lead to cleaner code compared to using getters and setters. Here’s an example:

class A:
    def __init__(self, some):
        self._b = some

    def b(self):
        return self._b

    def b(self, val):
        self._b = val

a = A('123')
a.b = '456'

Note that encapsulating heavy calculations or complex logic behind a property might confuse other developers who use your code.

Fix “Broken Windows” Immediately

If you come across a broken piece of code, it’s crucial to fix it right away to prevent small problems from escalating into larger ones. This strategy, known as the “zero defects methodology,” has been successfully adopted by companies like Microsoft and can greatly improve your production cycle.

Sure, here are three additional Python best practices:

Avoid Using Single-Letter Variables

While it might seem convenient to use single-letter variable names, they can often lead to code that is difficult to understand and maintain. Instead, try to use descriptive names for your variables that convey their purpose. This will make your code more readable and self-explanatory. Remember, code is read far more times than it is written!

Use List Comprehensions

Python offers list comprehensions, which are a more efficient and readable way to create lists. They can replace multiline for-loops with a single line of code. For example:

squares = [x**2 for x in range(10)]

This line of code generates a list of the squares of all numbers from 0 to 9, and is much more concise than the equivalent for-loop.

Exception Handling

Always make use of Python’s built-in exception-handling features. Never allow your program to crash due to unhandled exceptions. Instead, use a try/except block to catch exceptions and deal with them appropriately. Additionally, only catch exceptions that you can handle and avoid using bare except clauses. For example:

except SpecificError as e:

In this example, risky_operation() is a function that might raise SpecificError. Instead of letting the program crash, we catch the exception and call handle_error(e), which is a function that handles the error.

Remember that using these best practices consistently will not only make your code more efficient and maintainable but will also make you a better Python developer.


While Python provides a powerful development platform, it’s important to use it wisely. While this list of best practices is not exhaustive, implementing them can help you get the most out of Python’s capabilities.